Description Usage Arguments Value Author(s) References See Also Examples

The data are divided into `v`

non-overlapping subsets of roughly equal size. Then, `boosting`

is applied on `(v-1)`

of the subsets. Finally, predictions are made for the left out subsets,
and the process is repeated for each of the `v`

subsets.

1 2 |

`formula` |
a formula, as in the |

`data` |
a data frame in which to interpret the variables named in |

`boos` |
if |

`v` |
An integer, specifying the type of v-fold cross validation. Defaults to 10.
If |

`mfinal` |
an integer, the number of iterations for which boosting is run
or the number of trees to use. Defaults to |

`coeflearn` |
if 'Breiman'(by default), |

`control` |
options that control details of the rpart algorithm. See rpart.control for more details. |

`par` |
if |

An object of class `boosting.cv`

, which is a list with the following components:

`class ` |
the class predicted by the ensemble classifier. |

`confusion ` |
the confusion matrix which compares the real class with the predicted one. |

`error ` |
returns the average error. |

Esteban Alfaro-Cortes [email protected], Matias Gamez-Martinez [email protected] and Noelia Garcia-Rubio [email protected]

Alfaro, E., Gamez, M. and Garcia, N. (2013): “adabag: An R Package for Classification with Boosting and Bagging”. Journal of Statistical Software, Vol 54, 2, pp. 1–35.

Alfaro, E., Garcia, N., Gamez, M. and Elizondo, D. (2008): “Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks”. Decision Support Systems, 45, pp. 110–122.

Breiman, L. (1998): "Arcing classifiers". The Annals of Statistics, Vol 26, 3, pp. 801–849.

Freund, Y. and Schapire, R.E. (1996): "Experiments with a new boosting algorithm". In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.

Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009): “Multi-class AdaBoost”. Statistics and Its Interface, 2, pp. 349–360.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## rpart library should be loaded
data(iris)
iris.boostcv <- boosting.cv(Species ~ ., v=2, data=iris, mfinal=5,
control=rpart.control(cp=0.01))
iris.boostcv[-1]
## rpart and mlbench libraries should be loaded
## Data Vehicle (four classes)
#This example has been hidden to fulfill execution time <5s
#data(Vehicle)
#Vehicle.boost.cv <- boosting.cv(Class ~.,data=Vehicle,v=5, mfinal=10, coeflearn="Zhu",
#control=rpart.control(maxdepth=5))
#Vehicle.boost.cv[-1]
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.